Overview

Dataset statistics

Number of variables12
Number of observations2966
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory304.1 KiB
Average record size in memory105.0 B

Variable types

Numeric12

Alerts

gross_revenue is highly overall correlated with qtde_invoices and 3 other fieldsHigh correlation
recency_days is highly overall correlated with qtde_invoicesHigh correlation
qtde_invoices is highly overall correlated with gross_revenue and 3 other fieldsHigh correlation
qtde_items is highly overall correlated with gross_revenue and 3 other fieldsHigh correlation
avg_ticket is highly overall correlated with avg_unique_basket_sizeHigh correlation
avg_recency_days is highly overall correlated with frequencyHigh correlation
frequency is highly overall correlated with avg_recency_daysHigh correlation
qtde_products is highly overall correlated with gross_revenue and 3 other fieldsHigh correlation
avg_basket_size is highly overall correlated with gross_revenue and 1 other fieldsHigh correlation
avg_unique_basket_size is highly overall correlated with avg_ticket and 1 other fieldsHigh correlation
avg_ticket is highly skewed (γ1 = 25.14994547)Skewed
frequency is highly skewed (γ1 = 24.93345733)Skewed
qtde_returns is highly skewed (γ1 = 21.34478203)Skewed
customer_id has unique valuesUnique
recency_days has 33 (1.1%) zerosZeros
qtde_returns has 1480 (49.9%) zerosZeros

Reproduction

Analysis started2024-07-31 18:56:16.254139
Analysis finished2024-07-31 18:56:52.375158
Duration36.12 seconds
Software versionpandas-profiling vv3.6.3
Download configurationconfig.json

Variables

customer_id
Real number (ℝ)

Distinct2966
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15270.195
Minimum12347
Maximum18287
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size49.2 KiB
2024-07-31T15:56:52.520476image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum12347
5-th percentile12619.25
Q113799.75
median15219
Q316769.5
95-th percentile17964.75
Maximum18287
Range5940
Interquartile range (IQR)2969.75

Descriptive statistics

Standard deviation1719.2353
Coefficient of variation (CV)0.11258764
Kurtosis-1.2057693
Mean15270.195
Median Absolute Deviation (MAD)1487.5
Skewness0.032598535
Sum45291399
Variance2955770
MonotonicityNot monotonic
2024-07-31T15:56:52.786171image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17850 1
 
< 0.1%
17420 1
 
< 0.1%
15862 1
 
< 0.1%
12583 1
 
< 0.1%
13748 1
 
< 0.1%
15100 1
 
< 0.1%
15291 1
 
< 0.1%
14688 1
 
< 0.1%
17809 1
 
< 0.1%
15311 1
 
< 0.1%
Other values (2956) 2956
99.7%
ValueCountFrequency (%)
12347 1
< 0.1%
12348 1
< 0.1%
12352 1
< 0.1%
12356 1
< 0.1%
12358 1
< 0.1%
12359 1
< 0.1%
12360 1
< 0.1%
12362 1
< 0.1%
12364 1
< 0.1%
12370 1
< 0.1%
ValueCountFrequency (%)
18287 1
< 0.1%
18283 1
< 0.1%
18282 1
< 0.1%
18277 1
< 0.1%
18276 1
< 0.1%
18274 1
< 0.1%
18273 1
< 0.1%
18272 1
< 0.1%
18270 1
< 0.1%
18269 1
< 0.1%

gross_revenue
Real number (ℝ)

Distinct2953
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2688.0635
Minimum6.2
Maximum279138.02
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.3 KiB
2024-07-31T15:56:53.079113image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum6.2
5-th percentile229.6575
Q1567.58
median1080.5
Q32300.9425
95-th percentile7123.2025
Maximum279138.02
Range279131.82
Interquartile range (IQR)1733.3625

Descriptive statistics

Standard deviation10134.006
Coefficient of variation (CV)3.7700025
Kurtosis397.6063
Mean2688.0635
Median Absolute Deviation (MAD)667.23
Skewness17.64355
Sum7972796.5
Variance1.0269808 × 108
MonotonicityNot monotonic
2024-07-31T15:56:53.372064image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1224.06 2
 
0.1%
2053.02 2
 
0.1%
889.93 2
 
0.1%
1314.45 2
 
0.1%
379.65 2
 
0.1%
745.06 2
 
0.1%
331 2
 
0.1%
716 2
 
0.1%
293 2
 
0.1%
178.96 2
 
0.1%
Other values (2943) 2946
99.3%
ValueCountFrequency (%)
6.2 1
< 0.1%
13.3 1
< 0.1%
36.56 1
< 0.1%
45 1
< 0.1%
52 1
< 0.1%
52.2 1
< 0.1%
52.2 1
< 0.1%
62.43 1
< 0.1%
68.84 1
< 0.1%
70.02 1
< 0.1%
ValueCountFrequency (%)
279138.02 1
< 0.1%
259657.3 1
< 0.1%
194390.79 1
< 0.1%
140336.83 1
< 0.1%
124564.53 1
< 0.1%
117210.08 1
< 0.1%
91062.38 1
< 0.1%
72708.09 1
< 0.1%
66653.56 1
< 0.1%
65039.62 1
< 0.1%

recency_days
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct272
Distinct (%)9.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.240391
Minimum0
Maximum373
Zeros33
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size46.3 KiB
2024-07-31T15:56:53.697719image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q111
median31
Q381
95-th percentile242
Maximum373
Range373
Interquartile range (IQR)70

Descriptive statistics

Standard deviation77.578516
Coefficient of variation (CV)1.2076283
Kurtosis2.7562973
Mean64.240391
Median Absolute Deviation (MAD)26
Skewness1.7934182
Sum190537
Variance6018.4262
MonotonicityNot monotonic
2024-07-31T15:56:53.998737image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 99
 
3.3%
4 87
 
2.9%
3 85
 
2.9%
2 84
 
2.8%
8 76
 
2.6%
10 67
 
2.3%
7 66
 
2.2%
9 66
 
2.2%
17 64
 
2.2%
16 55
 
1.9%
Other values (262) 2217
74.7%
ValueCountFrequency (%)
0 33
 
1.1%
1 99
3.3%
2 84
2.8%
3 85
2.9%
4 87
2.9%
5 43
1.4%
7 66
2.2%
8 76
2.6%
9 66
2.2%
10 67
2.3%
ValueCountFrequency (%)
373 2
0.1%
372 3
0.1%
371 1
 
< 0.1%
368 1
 
< 0.1%
366 4
0.1%
365 2
0.1%
364 1
 
< 0.1%
360 1
 
< 0.1%
359 1
 
< 0.1%
358 4
0.1%

qtde_invoices
Real number (ℝ)

Distinct56
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.7245448
Minimum1
Maximum206
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.3 KiB
2024-07-31T15:56:54.305402image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile17
Maximum206
Range205
Interquartile range (IQR)4

Descriptive statistics

Standard deviation8.8550241
Coefficient of variation (CV)1.5468521
Kurtosis191.01665
Mean5.7245448
Median Absolute Deviation (MAD)2
Skewness10.770861
Sum16979
Variance78.411451
MonotonicityNot monotonic
2024-07-31T15:56:54.568162image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 784
26.4%
3 498
16.8%
4 394
13.3%
5 236
 
8.0%
1 189
 
6.4%
6 173
 
5.8%
7 139
 
4.7%
8 98
 
3.3%
9 68
 
2.3%
10 55
 
1.9%
Other values (46) 332
11.2%
ValueCountFrequency (%)
1 189
 
6.4%
2 784
26.4%
3 498
16.8%
4 394
13.3%
5 236
 
8.0%
6 173
 
5.8%
7 139
 
4.7%
8 98
 
3.3%
9 68
 
2.3%
10 55
 
1.9%
ValueCountFrequency (%)
206 1
< 0.1%
199 1
< 0.1%
124 1
< 0.1%
97 1
< 0.1%
91 1
< 0.1%
90 1
< 0.1%
86 1
< 0.1%
72 1
< 0.1%
62 2
0.1%
60 1
< 0.1%

qtde_items
Real number (ℝ)

Distinct1674
Distinct (%)56.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1578.3631
Minimum2
Maximum196844
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.3 KiB
2024-07-31T15:56:54.850270image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile102.25
Q1294
median637.5
Q31397.75
95-th percentile4382
Maximum196844
Range196842
Interquartile range (IQR)1103.75

Descriptive statistics

Standard deviation5704.0916
Coefficient of variation (CV)3.6139286
Kurtosis517.49252
Mean1578.3631
Median Absolute Deviation (MAD)419.5
Skewness18.755747
Sum4681425
Variance32536661
MonotonicityNot monotonic
2024-07-31T15:56:55.212872image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
272 9
 
0.3%
114 9
 
0.3%
88 8
 
0.3%
310 8
 
0.3%
84 8
 
0.3%
192 8
 
0.3%
150 8
 
0.3%
146 7
 
0.2%
355 7
 
0.2%
219 7
 
0.2%
Other values (1664) 2887
97.3%
ValueCountFrequency (%)
2 2
0.1%
12 2
0.1%
16 1
< 0.1%
17 1
< 0.1%
18 1
< 0.1%
19 1
< 0.1%
20 1
< 0.1%
23 1
< 0.1%
25 1
< 0.1%
26 1
< 0.1%
ValueCountFrequency (%)
196844 1
< 0.1%
80238 1
< 0.1%
77373 1
< 0.1%
69973 1
< 0.1%
64549 1
< 0.1%
64124 1
< 0.1%
63312 1
< 0.1%
58283 1
< 0.1%
57768 1
< 0.1%
50255 1
< 0.1%

avg_ticket
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct2965
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.040788
Minimum2.1505882
Maximum4453.43
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.3 KiB
2024-07-31T15:56:55.475878image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum2.1505882
5-th percentile5.003268
Q113.230105
median17.995736
Q324.991714
95-th percentile90.174375
Maximum4453.43
Range4451.2794
Interquartile range (IQR)11.761609

Descriptive statistics

Standard deviation119.56867
Coefficient of variation (CV)3.6188202
Kurtosis812.47908
Mean33.040788
Median Absolute Deviation (MAD)6.0150748
Skewness25.149945
Sum97998.976
Variance14296.667
MonotonicityNot monotonic
2024-07-31T15:56:55.718896image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14.47833333 2
 
0.1%
18.15222222 1
 
< 0.1%
13.92736842 1
 
< 0.1%
16.29372093 1
 
< 0.1%
36.24411765 1
 
< 0.1%
29.78416667 1
 
< 0.1%
22.8792623 1
 
< 0.1%
20.51104167 1
 
< 0.1%
149.025 1
 
< 0.1%
21.47435897 1
 
< 0.1%
Other values (2955) 2955
99.6%
ValueCountFrequency (%)
2.150588235 1
< 0.1%
2.443153153 1
< 0.1%
2.525591398 1
< 0.1%
2.567074341 1
< 0.1%
2.570175439 1
< 0.1%
2.65 1
< 0.1%
2.681666667 1
< 0.1%
2.716478873 1
< 0.1%
2.764450262 1
< 0.1%
2.768733945 1
< 0.1%
ValueCountFrequency (%)
4453.43 1
< 0.1%
3202.92 1
< 0.1%
1687.2 1
< 0.1%
952.9875 1
< 0.1%
872.13 1
< 0.1%
841.0214493 1
< 0.1%
651.1683333 1
< 0.1%
640 1
< 0.1%
624.4 1
< 0.1%
615.75 1
< 0.1%

avg_recency_days
Real number (ℝ)

Distinct1258
Distinct (%)42.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean67.319484
Minimum1
Maximum366
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.3 KiB
2024-07-31T15:56:55.937383image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8
Q126
median48.267857
Q385.25
95-th percentile200.75
Maximum366
Range365
Interquartile range (IQR)59.25

Descriptive statistics

Standard deviation63.514838
Coefficient of variation (CV)0.94348373
Kurtosis4.9060172
Mean67.319484
Median Absolute Deviation (MAD)26.267857
Skewness2.0661053
Sum199669.59
Variance4034.1347
MonotonicityNot monotonic
2024-07-31T15:56:56.140805image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14 25
 
0.8%
70 21
 
0.7%
4 21
 
0.7%
7 20
 
0.7%
49 18
 
0.6%
35 18
 
0.6%
46 17
 
0.6%
21 17
 
0.6%
11 17
 
0.6%
5 16
 
0.5%
Other values (1248) 2776
93.6%
ValueCountFrequency (%)
1 16
0.5%
1.5 1
 
< 0.1%
2 13
0.4%
2.5 1
 
< 0.1%
2.601398601 1
 
< 0.1%
3 15
0.5%
3.321428571 1
 
< 0.1%
3.330357143 1
 
< 0.1%
3.5 2
 
0.1%
4 21
0.7%
ValueCountFrequency (%)
366 1
 
< 0.1%
365 1
 
< 0.1%
363 1
 
< 0.1%
362 1
 
< 0.1%
357 2
0.1%
356 1
 
< 0.1%
355 2
0.1%
352 1
 
< 0.1%
351 2
0.1%
350 3
0.1%

frequency
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct1224
Distinct (%)41.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.11343206
Minimum0.0054495913
Maximum17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.3 KiB
2024-07-31T15:56:56.355844image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.0054495913
5-th percentile0.008892186
Q10.016342095
median0.025889968
Q30.049295775
95-th percentile1
Maximum17
Range16.99455
Interquartile range (IQR)0.032953679

Descriptive statistics

Standard deviation0.40799982
Coefficient of variation (CV)3.5968652
Kurtosis991.9773
Mean0.11343206
Median Absolute Deviation (MAD)0.012159919
Skewness24.933457
Sum336.43949
Variance0.16646386
MonotonicityNot monotonic
2024-07-31T15:56:56.568076image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 197
 
6.6%
0.0625 18
 
0.6%
0.02777777778 17
 
0.6%
0.02380952381 16
 
0.5%
0.08333333333 15
 
0.5%
0.03448275862 15
 
0.5%
0.09090909091 15
 
0.5%
0.02941176471 14
 
0.5%
0.02127659574 13
 
0.4%
0.03571428571 13
 
0.4%
Other values (1214) 2633
88.8%
ValueCountFrequency (%)
0.005449591281 1
 
< 0.1%
0.005464480874 1
 
< 0.1%
0.005479452055 1
 
< 0.1%
0.005494505495 1
 
< 0.1%
0.005586592179 2
0.1%
0.005602240896 1
 
< 0.1%
0.005617977528 2
0.1%
0.00566572238 1
 
< 0.1%
0.005681818182 2
0.1%
0.005698005698 3
0.1%
ValueCountFrequency (%)
17 1
 
< 0.1%
3 1
 
< 0.1%
2 6
 
0.2%
1.142857143 1
 
< 0.1%
1 197
6.6%
0.75 1
 
< 0.1%
0.6666666667 3
 
0.1%
0.550802139 1
 
< 0.1%
0.5335120643 1
 
< 0.1%
0.5 3
 
0.1%

qtde_products
Real number (ℝ)

Distinct458
Distinct (%)15.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean121.30512
Minimum1
Maximum7665
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.3 KiB
2024-07-31T15:56:57.036557image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9
Q129
median66
Q3135
95-th percentile378.75
Maximum7665
Range7664
Interquartile range (IQR)106

Descriptive statistics

Standard deviation266.0748
Coefficient of variation (CV)2.1934341
Kurtosis351.26282
Mean121.30512
Median Absolute Deviation (MAD)43
Skewness15.649601
Sum359791
Variance70795.8
MonotonicityNot monotonic
2024-07-31T15:56:57.268526image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
28 43
 
1.4%
20 39
 
1.3%
29 35
 
1.2%
19 34
 
1.1%
15 33
 
1.1%
11 32
 
1.1%
25 32
 
1.1%
35 32
 
1.1%
34 30
 
1.0%
27 30
 
1.0%
Other values (448) 2626
88.5%
ValueCountFrequency (%)
1 5
 
0.2%
2 14
0.5%
3 15
0.5%
4 17
0.6%
5 26
0.9%
6 28
0.9%
7 18
0.6%
8 19
0.6%
9 26
0.9%
10 28
0.9%
ValueCountFrequency (%)
7665 1
< 0.1%
5668 1
< 0.1%
5095 1
< 0.1%
4397 1
< 0.1%
2674 1
< 0.1%
2366 1
< 0.1%
2060 1
< 0.1%
1814 1
< 0.1%
1662 1
< 0.1%
1635 1
< 0.1%

qtde_returns
Real number (ℝ)

SKEWED  ZEROS 

Distinct212
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.212407
Minimum0
Maximum9014
Zeros1480
Zeros (%)49.9%
Negative0
Negative (%)0.0%
Memory size46.3 KiB
2024-07-31T15:56:57.513495image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q39
95-th percentile99.5
Maximum9014
Range9014
Interquartile range (IQR)9

Descriptive statistics

Standard deviation268.82943
Coefficient of variation (CV)7.8576589
Kurtosis578.70741
Mean34.212407
Median Absolute Deviation (MAD)1
Skewness21.344782
Sum101474
Variance72269.261
MonotonicityNot monotonic
2024-07-31T15:56:57.773061image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1480
49.9%
1 164
 
5.5%
2 148
 
5.0%
3 105
 
3.5%
4 90
 
3.0%
6 78
 
2.6%
5 62
 
2.1%
12 51
 
1.7%
7 42
 
1.4%
8 42
 
1.4%
Other values (202) 704
23.7%
ValueCountFrequency (%)
0 1480
49.9%
1 164
 
5.5%
2 148
 
5.0%
3 105
 
3.5%
4 90
 
3.0%
5 62
 
2.1%
6 78
 
2.6%
7 42
 
1.4%
8 42
 
1.4%
9 41
 
1.4%
ValueCountFrequency (%)
9014 1
< 0.1%
6420 1
< 0.1%
4427 1
< 0.1%
3768 1
< 0.1%
3332 1
< 0.1%
2878 1
< 0.1%
2022 1
< 0.1%
2012 1
< 0.1%
1776 1
< 0.1%
1594 1
< 0.1%

avg_basket_size
Real number (ℝ)

Distinct1975
Distinct (%)66.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean235.47115
Minimum1
Maximum6009.3333
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.3 KiB
2024-07-31T15:56:58.004424image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile44
Q1102.5
median171.5
Q3280.65
95-th percentile598.475
Maximum6009.3333
Range6008.3333
Interquartile range (IQR)178.15

Descriptive statistics

Standard deviation283.1479
Coefficient of variation (CV)1.2024739
Kurtosis103.84362
Mean235.47115
Median Absolute Deviation (MAD)82.5
Skewness7.7401096
Sum698407.42
Variance80172.731
MonotonicityNot monotonic
2024-07-31T15:56:58.267101image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
114 11
 
0.4%
100 11
 
0.4%
73 10
 
0.3%
86 10
 
0.3%
82 9
 
0.3%
140 8
 
0.3%
189 8
 
0.3%
88 8
 
0.3%
81 7
 
0.2%
60 7
 
0.2%
Other values (1965) 2877
97.0%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3.333333333 1
< 0.1%
5.333333333 1
< 0.1%
5.666666667 1
< 0.1%
6.142857143 1
< 0.1%
7.5 1
< 0.1%
9 1
< 0.1%
9.5 1
< 0.1%
11 1
< 0.1%
ValueCountFrequency (%)
6009.333333 1
< 0.1%
4282 1
< 0.1%
3906 1
< 0.1%
3868.65 1
< 0.1%
2880 1
< 0.1%
2801 1
< 0.1%
2733.944444 1
< 0.1%
2518.769231 1
< 0.1%
2160.333333 1
< 0.1%
2082.225806 1
< 0.1%

avg_unique_basket_size
Real number (ℝ)

Distinct908
Distinct (%)30.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.141913
Minimum0.2
Maximum258
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.3 KiB
2024-07-31T15:56:58.472333image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile2
Q17.5073529
median13.452273
Q321.7875
95-th percentile45
Maximum258
Range257.8
Interquartile range (IQR)14.280147

Descriptive statistics

Standard deviation15.120193
Coefficient of variation (CV)0.88205988
Kurtosis30.500905
Mean17.141913
Median Absolute Deviation (MAD)6.5477273
Skewness3.4748159
Sum50842.913
Variance228.62025
MonotonicityNot monotonic
2024-07-31T15:56:58.743652image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9 42
 
1.4%
17 40
 
1.3%
8 40
 
1.3%
13 40
 
1.3%
14 39
 
1.3%
7.5 36
 
1.2%
12 36
 
1.2%
11 36
 
1.2%
7 35
 
1.2%
16 35
 
1.2%
Other values (898) 2587
87.2%
ValueCountFrequency (%)
0.2 1
 
< 0.1%
0.25 3
 
0.1%
0.3333333333 6
0.2%
0.4 1
 
< 0.1%
0.4090909091 1
 
< 0.1%
0.5 13
0.4%
0.5454545455 1
 
< 0.1%
0.5555555556 2
 
0.1%
0.5714285714 1
 
< 0.1%
0.5882352941 1
 
< 0.1%
ValueCountFrequency (%)
258 1
< 0.1%
168.5 1
< 0.1%
143 1
< 0.1%
127 1
< 0.1%
102 2
0.1%
100 1
< 0.1%
94 2
0.1%
93.5 1
< 0.1%
91.33333333 1
< 0.1%
91 1
< 0.1%

Interactions

2024-07-31T15:56:48.369161image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-31T15:56:16.821342image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-31T15:56:19.824936image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-31T15:56:22.345775image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-31T15:56:25.015949image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-31T15:56:27.455532image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-31T15:56:29.831102image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-31T15:56:32.074580image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-31T15:56:35.158396image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-31T15:56:38.399838image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-31T15:56:41.785321image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-31T15:56:45.272674image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-31T15:56:48.652752image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-31T15:56:17.115566image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-31T15:56:20.039535image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-31T15:56:22.582171image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-31T15:56:25.231771image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-31T15:56:27.657189image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-31T15:56:30.008159image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-31T15:56:32.271772image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-31T15:56:35.461488image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-31T15:56:38.696425image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-31T15:56:42.027984image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-31T15:56:45.500560image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-31T15:56:48.969689image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-31T15:56:17.324639image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-31T15:56:20.232994image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-31T15:56:22.806131image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-31T15:56:25.420029image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-31T15:56:27.848354image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-31T15:56:30.191798image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-31T15:56:32.455375image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-31T15:56:35.733009image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-31T15:56:38.968279image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-31T15:56:42.246383image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-31T15:56:45.747478image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-31T15:56:49.223275image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-31T15:56:17.590620image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-31T15:56:20.438862image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-31T15:56:23.032418image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-31T15:56:25.611360image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-31T15:56:28.051887image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-31T15:56:30.403016image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-31T15:56:32.725069image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-31T15:56:35.991302image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-31T15:56:39.211633image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-31T15:56:42.482079image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-31T15:56:45.999350image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-31T15:56:49.469419image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-31T15:56:17.782382image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-31T15:56:20.619842image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-31T15:56:23.316604image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-31T15:56:25.802644image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-31T15:56:28.250315image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-31T15:56:30.574209image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-31T15:56:32.943406image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-31T15:56:36.263857image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-31T15:56:39.448995image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-31T15:56:42.755516image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-31T15:56:46.233462image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-31T15:56:49.836873image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-31T15:56:17.998348image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-31T15:56:20.829387image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-31T15:56:23.554820image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-31T15:56:26.143079image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-31T15:56:28.450068image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-31T15:56:30.763698image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-31T15:56:33.344508image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-31T15:56:36.646187image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-31T15:56:39.719870image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-31T15:56:43.211675image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-31T15:56:46.483150image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-31T15:56:50.151466image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-31T15:56:18.248797image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-31T15:56:21.039011image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-31T15:56:23.777872image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-31T15:56:26.349613image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-31T15:56:28.625466image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-31T15:56:30.932596image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-31T15:56:33.549101image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-31T15:56:36.858174image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-31T15:56:39.971193image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-31T15:56:43.546281image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-31T15:56:46.737697image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-31T15:56:50.399200image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-31T15:56:18.533517image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-31T15:56:21.269220image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-31T15:56:24.004334image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-31T15:56:26.546943image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-31T15:56:28.833326image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-31T15:56:31.122401image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-31T15:56:33.775155image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-31T15:56:37.103545image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-31T15:56:40.250128image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-31T15:56:43.878166image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-31T15:56:46.981683image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-31T15:56:50.628907image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-31T15:56:18.804689image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-31T15:56:21.460017image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-31T15:56:24.214659image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-31T15:56:26.734047image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-31T15:56:29.037344image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-31T15:56:31.357428image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-31T15:56:34.013250image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-31T15:56:37.353572image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-31T15:56:40.545201image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-31T15:56:44.142365image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-31T15:56:47.239972image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-31T15:56:50.876422image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-31T15:56:19.059660image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-31T15:56:21.685191image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-31T15:56:24.427564image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-31T15:56:26.916814image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-31T15:56:29.256407image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-31T15:56:31.548825image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-31T15:56:34.336509image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-31T15:56:37.617875image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-31T15:56:40.936407image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-31T15:56:44.384563image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-31T15:56:47.568322image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-31T15:56:51.114920image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-31T15:56:19.260720image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-31T15:56:21.898565image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-31T15:56:24.603098image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-31T15:56:27.090191image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-31T15:56:29.430683image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-31T15:56:31.704758image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-31T15:56:34.635120image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-31T15:56:37.826192image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-31T15:56:41.229473image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-31T15:56:44.586905image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-31T15:56:47.844946image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-31T15:56:51.364455image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-31T15:56:19.467426image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-31T15:56:22.138682image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-31T15:56:24.802204image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-31T15:56:27.293222image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-31T15:56:29.646405image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-31T15:56:31.912111image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-31T15:56:34.907962image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-31T15:56:38.093854image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-31T15:56:41.531210image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-31T15:56:44.850142image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-31T15:56:48.141684image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2024-07-31T15:56:58.929236image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
customer_idgross_revenuerecency_daysqtde_invoicesqtde_itemsavg_ticketavg_recency_daysfrequencyqtde_productsqtde_returnsavg_basket_sizeavg_unique_basket_size
customer_id1.000-0.0780.0000.026-0.072-0.1300.018-0.0020.010-0.064-0.126-0.016
gross_revenue-0.0781.000-0.4130.7710.9250.251-0.2510.0930.7460.3740.5740.098
recency_days0.000-0.4131.000-0.503-0.4060.0490.1090.016-0.436-0.120-0.0960.014
qtde_invoices0.0260.771-0.5031.0000.7170.060-0.2590.0800.6930.2960.101-0.185
qtde_items-0.0720.925-0.4060.7171.0000.171-0.2310.0840.7330.3460.7290.141
avg_ticket-0.1300.2510.0490.0600.1711.000-0.1230.092-0.3710.1890.193-0.616
avg_recency_days0.018-0.2510.109-0.259-0.231-0.1231.000-0.881-0.169-0.399-0.0820.130
frequency-0.0020.0930.0160.0800.0840.092-0.8811.0000.0380.2350.031-0.121
qtde_products0.0100.746-0.4360.6930.733-0.371-0.1690.0381.0000.2480.3830.508
qtde_returns-0.0640.374-0.1200.2960.3460.189-0.3990.2350.2481.0000.212-0.054
avg_basket_size-0.1260.574-0.0960.1010.7290.193-0.0820.0310.3830.2121.0000.396
avg_unique_basket_size-0.0160.0980.014-0.1850.141-0.6160.130-0.1210.508-0.0540.3961.000

Missing values

2024-07-31T15:56:51.746560image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2024-07-31T15:56:52.188703image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

customer_idgross_revenuerecency_daysqtde_invoicesqtde_itemsavg_ticketavg_recency_daysfrequencyqtde_productsqtde_returnsavg_basket_sizeavg_unique_basket_size
0178505391.21372.034.01733.018.15222235.50000017.000000297.037.050.9705880.588235
1130473232.5956.09.01390.018.90403527.2500000.028302171.035.0154.44444411.666667
2125836705.382.015.05028.028.90250023.1875000.040323232.050.0335.2000007.600000
313748948.2595.05.0439.033.86607192.6666670.01792128.00.087.8000004.800000
415100876.00333.03.080.0292.0000008.6000000.0731713.022.026.6666670.333333
5152914623.3025.014.02102.045.32647123.2000000.040115102.029.0150.1428574.357143
6146885579.107.021.03584.017.21944418.3000000.057221324.0399.0170.6666676.857143
7178095411.9116.012.02057.088.71983635.7000000.03352061.041.0171.4166673.750000
81531160632.750.091.038147.025.6266914.1444440.2433162366.0474.0419.1978025.978022
9160982005.6387.07.0613.029.93477647.6666670.02439067.00.087.5714294.714286
customer_idgross_revenuerecency_daysqtde_invoicesqtde_itemsavg_ticketavg_recency_daysfrequencyqtde_productsqtde_returnsavg_basket_sizeavg_unique_basket_size
5602177271060.2515.01.0645.016.0643946.01.00000066.06.0645.00000066.000000
561217232417.772.02.0202.011.93628612.00.15384635.00.0101.00000015.000000
561317468137.0010.02.0116.027.4000004.00.4000005.00.058.0000002.500000
562413596664.115.02.0384.04.5486997.00.250000146.00.0192.00000066.500000
5630148931237.859.02.0799.016.9568492.00.66666773.00.0399.50000034.000000
563412479473.2011.01.0382.015.7733334.01.00000030.034.0382.00000029.000000
565514126706.137.03.0508.047.0753333.00.75000015.050.0169.3333334.666667
5661135211070.471.03.0715.02.5670744.50.300000417.00.0238.333333102.000000
567115060293.008.04.0256.02.5701751.02.000000114.00.064.00000019.250000
569012558269.967.01.0196.024.5418186.01.00000011.0196.0196.00000011.000000